import logging
from pathlib import Path
from typing import Dict, Any
import random
import numpy as np
import cv2
import torch
import torch.nn as nn

logger = logging.getLogger(__name__)

class ModelInference:
    """Model inference class"""
    
    def __init__(self, config):
        self.config = config
        self.demo_mode = config.get('model.demo_mode', True)
        
        # Model related attributes
        self.model = None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        
        # Preprocessing parameters
        self.edge_low = config.get('model.preprocess.edge_low', 100)
        self.edge_high = config.get('model.preprocess.edge_high', 200)
        self.min_area = config.get('model.preprocess.min_area', 50)
        
        if self.demo_mode:
            logger.info("Running in demo mode - using mock predictions")
        else:
            self.load_model()
            
    def _config_model(self):
        """Configure model settings"""
        try:
            # Set device
            logger.info(f"Using device: {self.device}")
            
            # Set to evaluation mode
            if self.model is not None:
                self.model.to(self.device)
                self.model.eval()
                
        except Exception as e:
            logger.error(f"Failed to configure model: {e}")
            raise
            
    def load_model(self):
        """Load model checkpoint"""
        try:
            checkpoint_dir = self.config.get('model.checkpoint_dir')
            if not checkpoint_dir:
                raise ValueError("Model checkpoint directory not specified")
                
            # Load model weights from checkpoint 31
            weights_path = Path(checkpoint_dir) / 'checkpoint-31.pth'
            if not weights_path.exists():
                raise ValueError(f"Model weights not found at {weights_path}")
                
            # TODO: Initialize model architecture and load weights
            # self.model = YourModelClass()
            # self.model.load_state_dict(torch.load(weights_path))
            # self.model.eval()
            
            logger.info(f"Successfully loaded model from {weights_path}")
            
        except Exception as e:
            logger.error(f"Failed to load model: {e}")
            raise
            
    def predict(self, image: np.ndarray) -> Dict[str, Any]:
        """Run inference on image"""
        try:
            if self.demo_mode:
                return self._mock_predict()
            
            # Preprocess image
            processed_image = self._preprocess_image(image)
            
            # Convert to tensor and move to device
            input_tensor = torch.from_numpy(processed_image).to(self.device)
            
            # Run model inference
            if self.model is None:
                logger.warning("Model not loaded, using mock prediction")
                return self._mock_predict()
                
            with torch.no_grad():
                predictions = self.model(input_tensor)
            
            # Process predictions
            # Note: Adjust this based on your model's actual output format
            count = float(len(predictions['boxes'])) if 'boxes' in predictions else 0.0
            confidence = float(predictions['scores'].mean()) if 'scores' in predictions else 0.0
            boxes = predictions.get('boxes', []).cpu().numpy().tolist()
            scores = predictions.get('scores', []).cpu().numpy().tolist()
            
            # Calculate statistics
            stats = {
                'count_stats': {
                    'mean': count,
                    'std': 0.0,  # TODO: Calculate from multiple runs
                    'cv': 0.0,   # TODO: Calculate from multiple runs
                    'min': count,
                    'max': count,
                    'median': count,
                    'q1': count,
                    'q3': count
                },
                'confidence_stats': {
                    'mean': confidence,
                    'std': 0.0,
                    'min': confidence,
                    'max': confidence
                }
            }
            
            return {
                'count': count,
                'confidence': confidence,
                'boxes': boxes,
                'scores': scores,
                'statistics': stats
            }
            
        except Exception as e:
            logger.error(f"Prediction failed: {e}")
            raise
            
    def _preprocess_image(self, image: np.ndarray) -> np.ndarray:
        """Preprocess image for model input"""
        try:
            # Convert to grayscale if needed
            if len(image.shape) == 3:
                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            else:
                gray = image.copy()

            # Initial Gaussian blur
            blurred = cv2.GaussianBlur(gray, (5, 5), 1.0)

            # Edge detection with dynamic thresholds
            edges = cv2.Canny(blurred, self.edge_low, self.edge_high)

            # Initial binary threshold using Otsu's method
            _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

            # Apply watershed algorithm
            processed = self._apply_watershed(binary)

            # Normalize to [0, 1]
            processed = processed.astype(np.float32) / 255.0

            # Add batch dimension
            processed = np.expand_dims(processed, axis=0)

            return processed
            
        except Exception as e:
            logger.error(f"Image preprocessing failed: {e}")
            raise
            
    def _mock_predict(self) -> Dict[str, Any]:
        """Generate mock prediction results for demo mode"""
        try:
            count = float(random.randint(50, 200))
            confidence = float(random.uniform(0.8, 0.99))
            
            return {
                'count': count,
                'confidence': confidence,
                'boxes': [],  # Placeholder for detection boxes
                'scores': [],  # Placeholder for detection scores
                'statistics': {
                    'count_stats': {
                        'mean': count,
                        'std': 0.0,
                        'cv': 0.0,
                        'min': count,
                        'max': count,
                        'median': count,
                        'q1': count,
                        'q3': count
                    },
                    'confidence_stats': {
                        'mean': confidence,
                        'std': 0.0,
                        'min': confidence,
                        'max': confidence
                    }
                }
            }
        except Exception as e:
            logger.error(f"Mock prediction failed: {e}")
            raise ValueError("Failed to generate mock prediction")
